U.S. patent application number 13/840438 was filed with the patent office on 2014-09-18 for reducing digest storage consumption by tracking similarity elements in a data deduplication system.
This patent application is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Lior ARONOVICH.
Application Number | 20140279954 13/840438 |
Document ID | / |
Family ID | 51533011 |
Filed Date | 2014-09-18 |
United States Patent
Application |
20140279954 |
Kind Code |
A1 |
ARONOVICH; Lior |
September 18, 2014 |
REDUCING DIGEST STORAGE CONSUMPTION BY TRACKING SIMILARITY ELEMENTS
IN A DATA DEDUPLICATION SYSTEM
Abstract
For reducing digests storage consumption in a data deduplication
system using a processor device in a computing environment, input
data is partitioned into chunks, and the chunks are grouped into
chunk sets. Digests are calculated for input data and stored in
sets corresponding to the chunk sets. Similarity elements are
calculated for the input data and the similarity elements are
stored in a similarity search structure. The number of similarity
elements associated with a chunk set which are currently contained
in the similarity search structure is maintained for each chunk
set, and when this number of a specific chunk set becomes lower
than a threshold, the digests set associated with that chunk set
are removed from the repository.
Inventors: |
ARONOVICH; Lior; (Toronto,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION
Armonk
NY
|
Family ID: |
51533011 |
Appl. No.: |
13/840438 |
Filed: |
March 15, 2013 |
Current U.S.
Class: |
707/692 |
Current CPC
Class: |
G06F 16/2365 20190101;
G06F 16/215 20190101; G06F 16/955 20190101; G06F 3/0641 20130101;
G06F 16/278 20190101; G06F 16/1752 20190101; G06F 16/285
20190101 |
Class at
Publication: |
707/692 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A method for reducing digests storage consumption by tracking
numbers of similarity elements in a similarity search structure in
a data deduplication system using a processor device in a computing
environment, comprising: partitioning input data into chunks and
grouping the chunks into chunk sets; calculating digests for the
input data and storing the digests in sets corresponding to the
chunk sets; calculating similarity elements for the input data and
storing the similarity elements in a similarity search structure;
maintaining for each one of the chunk sets a number of the
similarity elements associated with the chunk set which are
currently contained in the similarity search structure; and
removing a digests set associated with a chunk set from the
repository when the number of similarity elements of that chunk set
becomes lower than a threshold.
2. The method of claim 1, further including defining the chunk sets
to be non-overlapping and covering together all of the chunks.
3. The method of claim 1, further including using the similarity
elements to find repository data which is similar to the input
data.
4. The method of claim 3, further including using input digests and
repository digests to calculate data matches.
5. The method of claim 4, further including removing the similarity
elements of repository data that was matched with later ingested
data from the similarity search structure.
6. The method of claim 5, further including, for each chunk set
enclosing matched repository data, subtracting the number of
similarity elements which were removed from the similarity search
structure for the chunk set from a maintained number of similarity
elements in the similarity search structure associated with the
chunk set.
7. The method of claim 1, further including storing the digest
values in the repository linearly in a sequence of occurrence of
the digest values in data.
8. The method of claim 1, further including storing the digest
values in the repository in a form that is independent of the form
by which the data that the digest values describe is stored.
9. A system for reducing digests storage consumption by tracking
numbers of similarity elements in a similarity search structure in
a data deduplication system of a computing environment, the system
comprising: the data deduplication system; a repository operating
in the data deduplication system; a similarity search structure in
association with the repository in the data deduplication system;
and at least one processor device operable in the computing storage
environment for controlling the data deduplication system, wherein
the at least one processor device: partitions input data into
chunks and grouping the chunks into chunk sets, calculates digests
for the input data and storing the digests in sets corresponding to
the chunk sets, calculates similarity elements for the input data
and storing the similarity elements in the similarity search
structure, maintains for each one of the chunk sets a number of the
similarity elements associated with the chunk set which are
currently contained in the similarity search structure, and removes
a digests set associated with that chunk set from the repository
when a number of specific chunk sets becomes lower than a
predetermined threshold.
10. The system of claim 9, wherein the at least one processor
device defines the chunk sets to be non-overlapping and covering
together all of the chunks.
11. The system of claim 9, wherein the at least one processor
device uses the similarity elements to find repository data which
is similar to the input data.
12. The system of claim 11, wherein the at least one processor
device uses input digests and repository digests to calculate data
matches.
13. The system of claim 12, wherein the at least one processor
device removes the similarity elements of repository data that was
matched with later ingested data from the similarity search
structure.
14. The system of claim 13, wherein the at least one processor
device, for each chunk set enclosing matched repository data,
subtracts the number of similarity elements which were removed from
the similarity search structure for the chunk set from a maintained
number of similarity elements in the similarity search structure
associated with the chunk set.
15. The system of claim 9, wherein the at least one processor
device stores the digest values in the repository linearly in a
sequence of occurrence of the digest values in data.
16. The system of claim 9, wherein the at least one processor
device stores the digest values in the repository in a form that is
independent of the form by which the data that the digest values
describe is stored.
17. A computer program product for reducing digests storage
consumption by tracking the numbers of similarity elements in a
similarity search structure in a data deduplication system using a
processor device in a computing environment, the computer program
product comprising a computer-readable storage medium having
computer-readable program code portions stored therein, the
computer-readable program code portions comprising: a first
executable portion that partitions input data into chunks and
grouping the chunks into chunk sets; a second executable portion
that calculates digests for the input data and storing the digests
in sets corresponding to the chunk sets; a third executable portion
that calculates similarity elements for the input data and storing
the similarity elements in a similarity search structure; a fourth
executable portion that maintains for each one of the chunk sets a
number of the similarity elements associated with the chunk set
which are currently contained in the similarity search structure;
and a fifth executable portion that removes a digests set
associated with a chunk set from the repository when the number of
similarity elements of that chunk set becomes lower than a
threshold.
18. The computer program product of claim 17, further including a
sixth executable portion that defines the chunk sets to be
non-overlapping and covering together all of the chunks.
19. The computer program product of claim 17, further including a
sixth executable portion that uses the similarity elements to find
repository data which is similar to the input data.
20. The computer program product of claim 19, further including a
seventh executable portion that uses input digests and repository
digests to calculate data matches.
21. The computer program product of claim 20, further including an
eighth executable portion that removes the similarity elements of
repository data that was matched with later ingested data from the
similarity search structure.
22. The computer program product of claim 21, further including a
ninth executable portion that, for each chunk set enclosing matched
repository data, subtracts the number of similarity elements which
were removed from the similarity search structure for the chunk set
from a maintained number of similarity elements in the similarity
search structure associated with the chunk set.
23. The computer program product of claim 17, further including a
sixth executable portion that stores the digest values in the
repository linearly in a sequence of occurrence of the digest
values in data.
24. The computer program product of claim 17, further including a
sixth executable portion that stores the digest values in the
repository in a form that is independent of the form by which the
data that the digest values describe is stored.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention relates in general to computers, and
more particularly to reducing digests storage consumption by
tracking the numbers of similarity elements in a similarity search
structure for removing digests in a data deduplication system in a
computing environment.
[0003] 2. Description of the Related Art
[0004] In today's society, computer systems are commonplace.
Computer systems may be found in the workplace, at home, or at
school. Computer systems may include data storage systems, or disk
storage systems, to process and store data. Large amounts of data
have to be processed daily and the current trend suggests that
these amounts will continue being ever-increasing in the
foreseeable future. An efficient way to alleviate the problem is by
using deduplication. The idea underlying a deduplication system is
to exploit the fact that large parts of the available data are
copied again and again, by locating repeated data and storing only
its first occurrence. Subsequent copies are replaced with pointers
to the stored occurrence, which significantly reduces the storage
requirements if the data is indeed repetitive.
SUMMARY OF THE DESCRIBED EMBODIMENTS
[0005] In one embodiment, a method is provided for tracking of a
number of similarity elements currently in a similarity search
structure for removing digests from a repository in a data
deduplication system using a processor device in a computing
environment. In one embodiment, by way of example only, a
deduplication process includes calculating digests for the input
data and storing said digests in sets corresponding to chunk sets.
Similarity elements are calculated for the input data and the
similarity elements are stored in a similarity search structure.
The number of similarity elements associated with each chunk set
which are currently contained in the similarity search structure is
maintained for each chunk set, and when this number of specific
chunk set becomes lower than a threshold, the digests set
associated with that chunk set are removed from the repository.
[0006] In another embodiment, a computer system is provided for
tracking of a number of similarity elements currently in the
similarity search structure for removing digests from a repository
in a data deduplication system using a processor device, in a
computing environment. The computer system includes a
computer-readable medium and a processor in operable communication
with the computer-readable medium. In one embodiment, by way of
example only, the processor calculates digests for the input data
and storing said digests in sets corresponding to the chunk sets.
Similarity elements are calculated for the input data and the
similarity elements are stored in a similarity search structure.
The number of similarity elements associated with each chunk set
which are currently contained in the similarity search structure is
maintained for each chunk set, and when this number of specific
chunk set becomes lower than a threshold, the digests set
associated with that chunk set are removed from the repository.
[0007] In a further embodiment, a computer program product is
provided for tracking of a number of similarity elements currently
in the similarity search structure for removing digests from a
repository in a data deduplication system in a data deduplication
system using a processor device, in a computing environment. The
computer-readable storage medium has computer-readable program code
portions stored thereon. The computer-readable program code
portions include a first executable portion that calculates digests
for the input data and storing said digests in sets corresponding
to the chunk sets. Similarity elements are calculated for the input
data and the similarity elements are stored in a similarity search
structure. The number of similarity elements associated with each
chunk set which are currently contained in the similarity search
structure is maintained for each chunk set, and when this number of
specific chunk set becomes lower than a threshold, the digests set
associated with that chunk set are removed from the repository.
[0008] In addition to the foregoing exemplary method embodiment,
other exemplary system and computer product embodiments are
provided and supply related advantages. The foregoing summary has
been provided to introduce a selection of concepts in a simplified
form that are further described below in the Detailed Description.
This Summary is not intended to identify key features or essential
features of the claimed subject matter, nor is it intended to be
used as an aid in determining the scope of the claimed subject
matter. The claimed subject matter is not limited to
implementations that solve any or all disadvantages noted in the
background.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] In order that the advantages of the invention will be
readily understood, a more particular description of the invention
briefly described above will be rendered by reference to specific
embodiments that are illustrated in the appended drawings.
Understanding that these drawings depict embodiments of the
invention and are not therefore to be considered to be limiting of
its scope, the invention will be described and explained with
additional specificity and detail through the use of the
accompanying drawings, in which:
[0010] FIG. 1 is a block diagram illustrating a computing system
environment having an example storage device in which aspects of
the present invention may be realized;
[0011] FIG. 2 is a block diagram illustrating a hardware structure
of data storage system in a computer system in which aspects of the
present invention may be realized;
[0012] FIG. 3 is a flowchart illustrating an exemplary method for
digest retrieval based on similarity search in deduplication
processing in a data deduplication system in which aspects of the
present invention may be realized;
[0013] FIG. 4 is a flowchart illustrating an exemplary alternative
method for digest retrieval based on similarity search in
deduplication processing in a data deduplication system in which
aspects of the present invention may be realized;
[0014] FIG. 5 is a flowchart illustrating an exemplary method for
efficient calculation of both similarity search values and
boundaries of digest blocks using a single linear calculation of
rolling hash values in a data deduplication system in which aspects
of the present invention may be realized;
[0015] FIG. 6 is a block diagram illustrating a compact data
structure containing a record for each chunk set in which aspects
of the present invention may be realized;
[0016] FIG. 7 is a flowchart illustrating an exemplary method for
reducing digests storage consumption in a data deduplication system
in which aspects of the present invention may be realized;
[0017] FIG. 8 is a flowchart illustrating an alternative exemplary
method for reducing digests storage consumption in a data
deduplication system in which aspects of the present invention may
be realized; and
[0018] FIG. 9 is a flowchart illustrating an exemplary method 300
for tracking of the number of similarity elements currently in a
similarity search structure for removing the digests associated
with that chunk set from the repository in a data deduplication
system in which aspects of the present invention may be
realized.
DETAILED DESCRIPTION OF THE DRAWINGS
[0019] Data deduplication is a highly important and vibrant field
in computing storage systems. Data deduplication refers to the
reduction and/or elimination of redundant data. In data
deduplication, a data object, which may be a file, a data stream,
or some other form of data, is broken down into one or more parts
called chunks or blocks. In a data deduplication process, duplicate
copies of data are reduced or eliminated, leaving a minimal amount
of redundant copies, or a single copy of the data, respectively.
The goal of a data deduplication system is to store a single copy
of duplicated data, and the challenges in achieving this goal are
efficiently finding the duplicate data patterns in a typically
large repository, and storing the data patterns in a storage
efficient deduplicated form. A significant challenge in
deduplication storage systems is scaling to support very large
repositories of data. Such large repositories can reach sizes of
Petabytes (1 Petabyte=2.sup.50 bytes) or more. Deduplication
storage systems supporting such repository sizes, must provide
efficient processing for finding duplicate data patterns within the
repositories, where efficiency is measured in resource consumption
for achieving deduplication (resources may be CPU cycles, RAM
storage, persistent storage, networking, etc.). In one embodiment,
a deduplication storage system may be based on maintaining a search
optimized index of values known as fingerprints or digests, where a
(small) fingerprint represents a (larger) block of data in the
repository. The fingerprint values may be cryptographic hash values
calculated based on the blocks' data. In one embodiment, secure
hash algorithm (SHA), e.g. SHA-1 or SHA-256, which are a family of
cryptographic hash functions, may be used. Identifying fingerprint
matches, using index lookup, enables to store references to data
that already exists in a repository.
[0020] To provide reasonable deduplication in this approach, the
mean size of the data blocks based on which fingerprints are
generated must be limited to smaller sizes and may not be too
large. The reason being that a change of a bit within a data block
will probabilistically change the data block's corresponding
fingerprint, and thus having large data blocks makes the scheme
more sensitive to updates in the data as compared to having small
blocks. A typical data block size may range from 4 KB to 64 KB,
depending on the type of application and workload. Thus, by way of
example only, small data blocks may range in sizes of up to 64 KB,
and large data blocks are those data blocks having a size larger
than 64 KB.
[0021] To support very large repositories scaling to Petabytes
(e.g., repositories scaling to at least one Petabyte), the number
of fingerprints to store coupled with the size of a fingerprint
(ranging between 16 bytes and 64 bytes), becomes prohibitive. For
example, for 1 Petabyte of deduplicated data, with a 4 KB mean data
block size, and 32 bytes fingerprint size (e.g. of SHA-256), the
storage required to store the fingerprints is 8 Terabytes.
Maintaining a search optimized data structure for such volumes of
fingerprints is difficult, and requires optimization techniques.
However existing optimization techniques do not scale to these
sizes while maintaining performance. For this reason, to provide
reasonable performance, the supported repositories have to be
relatively small (on the order of tens of TB). Even for such
smaller sizes, considerable challenges and run-time costs arise due
to the large scale of the fingerprint indexes, that create a
bottle-neck in deduplication processing.
[0022] To solve this problem, in one embodiment, a deduplication
system may be based on a two step approach for searching data
patterns during deduplication. In the first step, a large chunk of
incoming data (e.g. a few megabytes) is searched in the repository
for similar (rather than identical) data chunks of existing data,
and the incoming data chunk is partitioned accordingly into
intervals and paired with corresponding (similar) repository
intervals. In the second step, a byte-wise matching algorithm is
applied on pairs of similar intervals, to identify identical
sub-intervals, which are already stored in a repository of data.
The matching algorithm of the second step relies on reading all the
relevant similar data in the repository in order to compare it
byte-wise to the input data.
[0023] Yet, a problem stemming from a byte-wise comparison of data
underlying the matching algorithm of the second step, is that data
of roughly the same size and rate as the incoming data should be
read from the repository, for comparison purposes. For example, a
system processing 1 GB of incoming data per second, should read
about 1 GB of data per second from the repository for byte-wise
comparison. This requires substantially high capacities of I/O per
second of the storage devices storing the repository data, which in
turn increases their cost.
[0024] Additional trends in information technology coinciding with
the above problem are the following: (1) Improvements in the
computing ability by increasing CPU speeds and the number of CPU
cores. (2) Increase in disk density, while disk throughput remains
relatively constant or improving only modestly. This means that
there are fewer spindles relative to the data capacity, thus
practically reducing the overall throughput. Due to the problem
specified above, there is a need to design an alternative solution,
to be integrated in a two step deduplication system embodiment
specified above, that does not require reading from the repository
in high rates/volumes.
[0025] Therefore, additional embodiments, by way of example only,
address this problem, as well as shift resource consumption from
disks to the CPUs, to benefit from the above trends. The
embodiments described herein are integrated within the two step and
scalable deduplication embodiments described above, and use a
similarity search to focus lookup of digests during deduplication.
In one embodiment, a global similarity search is used as a basis
for focusing the search for digests of repository data that is most
likely to match input data.
[0026] The embodiments described herein significantly reduce the
capacity of I/O per second required of underlying disks, benefit
from the increases in computing ability and in disk density, and
considerably reduce the costs of processing, as well as maintenance
costs and environmental overhead (e.g. power consumption).
[0027] In one embodiment, input data is segmented into small
segments (e.g. 4 KB) and a digest (a cryptographic hash value, e.g.
SHA1) is calculated for each such segment. First, a similarity
search algorithm, as described above, is applied on an input chunk
of data (e.g. 16 MB), and the positions of the most similar
reference data in the repository are found. These positions are
then used to lookup the digests of the similar reference data. The
digests of all the data contained in the repository are stored and
retrieved in a form that corresponds to their occurrence in the
data. Given a position of a section of data contained in the
repository, the data's associated digests are efficiently located
in the repository and retrieved. Next, these reference digests are
loaded into memory, and instead of comparing data to find matches,
the input digests and the loaded reference digests are matched.
[0028] Thus, in one embodiment, a new fundamental approach for a
data deduplication system, integrates a scalable two step approach
of similarity search followed by a search of identical matching
segments, that was first based on byte-wise data comparison, with a
considerably more efficient and cost effective digest/fingerprint
based matching algorithm, which enables to read only a small
fraction (e.g., 1%) of the volume previously required. With the new
present invention specified herein, the deduplication system
preserves its unique scalability to very large data repositories,
while gaining considerable additional efficiency, improving
performance and reducing the costs of the underlying hardware.
[0029] In one embodiment, by way of example only, the term "similar
data" may be referred to as: for any given input data, data which
is similar to the input data is defined as data which is mostly the
same (i.e. not entirely but at least 50% similar) as the input
data. By looking at the data in a binary view (perspective), this
means that similar data is data where most (i.e. not entirely but
at least 50% similar) of the bytes are the same as the input
data.
[0030] In one embodiment, by way of example only, the term
"similarity search" may be referred to as the process of searching
for data which is similar to input data in a repository of data. In
one embodiment, this process may be performed using a search
structure of similarity elements, which is maintained and searched
within.
[0031] In one embodiment, by way of example only, the term
"similarity elements" refers to computational elements that may be
calculated based on the data and facilitate a global search for
data which is similar to input data in a repository of data. In
general, one or more similarity elements are calculated, and
represent, a large (e.g. at least 16 MB) chunk of data.
[0032] Thus, the various embodiments described herein provide
various solutions for digest retrieval based on a similarity search
in deduplication processing in a data deduplication system using a
processor device in a computing environment. In one embodiment, by
way of example only, input data is partitioned into fixed sized
data chunks. Similarity elements, digest block boundaries and
digest values are calculated for each of the fixed sized data
chunks. Matching similarity elements are searched for in a search
structure (i.e. index) containing the similarity elements for each
of the fixed sized data chunks in a repository of data. Positions
of similar data are located in a repository. The positions of the
similar data are used to locate and load into the memory stored
digest values and corresponding stored digest block boundaries of
the similar data in the repository. It should be noted that in one
embodiment the positions may be either physical or logical (i.e.
virtual). The positions are of data inside a repository of data. An
important property of a `position` is that given a position
(physical or logical) in the repository's data, the data in that
position can be efficiently located and accessed. The digest values
and the corresponding digest block boundaries of the input data are
matched with the stored digest values and the corresponding stored
digest block boundaries to find data matches.
[0033] Thus, the various embodiments described herein provide
various solutions for digest retrieval based on a similarity search
in deduplication processing in a data deduplication system using a
processor device in a computing environment. In one embodiment, by
way of example only, input data is partitioned into fixed sized
data chunks. Similarity elements, digest block boundaries and
digest values are calculated for each of the fixed sized data
chunks. Matching similarity elements are searched for in a search
structure (i.e. index) containing the similarity elements for each
of the fixed sized data chunks in a repository of data. Positions
of similar data are located in a repository. The positions of the
similar data are used to locate and load into the memory stored
digest values and corresponding stored digest block boundaries of
the similar data in the repository. The digest values and the
corresponding digest block boundaries of the input data are matched
with the stored digest values and the corresponding stored digest
block boundaries to find data matches.
[0034] In one embodiment, the present invention provides a solution
for utilizing a similarity search to load into memory relevant
digests from the repository, for efficient deduplication
processing. In a data deduplication system, deduplication is
performed by partitioning the data into large fixed sized chunks,
and for each chunk calculating similarity elements, digest blocks
and corresponding digest values. The data deduplication system
searches for matching similarity elements in a search structure of
similarity elements, and finds the positions of similar data in the
repository. The data deduplication system uses these positions of
similar data to locate and load into memory stored digests of the
similar repository data, and then matches input and repository
digest values to find data matches.
[0035] In one embodiment, the present invention provides for
efficient calculation of both similarity search elements and
segmentation (i.e. boundaries) of digest blocks, using a single
linear calculation of rolling hash values. In a data deduplication
system, the input data is partitioned into chunks, and for each
chunk a set of rolling hash values is calculated. A single linear
scan of the rolling hash values produces both similarity search
elements and boundaries of the digest blocks of the chunk. Each
rolling hash value corresponds to a consecutive window of bytes in
byte offsets. The similarity search elements are used to search for
similar data in the repository. The digest blocks segmentation is
used to calculate digest values of the chunk for digests matching.
Each rolling hash value contributes to the calculation of the
similarity elements and the calculation of the digest blocks
segmentations. Each rolling hash value is discarded after
contributing to the calculation.
[0036] Thus, as described above, the deduplication approach of the
present invention uses a two step process for searching data
patterns during deduplication. In the first step, a large chunk of
incoming data (e.g. 16 megabytes "MB") is searched in the
repository for similar (rather than identical) chunks of existing
data, and the incoming chunk is partitioned accordingly into
intervals, and paired with corresponding (similar) repository
intervals. The similarity search structure (or "index") used in the
first step is compact and simple to maintain and search within,
because the elements used for a similarity search are very compact
relative to the data they represent (e.g. 16 bytes representing 4
megabytes). Further included in the first step, in addition to a
calculation of similarity elements, is a calculation of digest
segments and respective digest values for the input chunk of data.
All these calculations are based on a single calculation of rolling
hash values. In the second step, reference digests of the similar
repository intervals are retrieved, and then the input digests are
matched with the reference digests, to identify data matches.
[0037] A significant problem arising in similarity based
deduplication, is the size of the storage space consumed by digests
stored in a repository. In one embodiment, for similarity based
deduplication, a relatively small average digest segment size is
used (e.g. 1 KB-2 KB). The reason for that is twofold. First, a
small digest segment size provides significant benefits: it enables
to achieve higher resolution during deduplication; it is less
sensitive to modifications in the data; and it enables to lose less
deduplication potential at the edges of each data mismatch. For
these reasons, a small digest segment size improves the
deduplication results. Second, in similarity-based deduplication,
there is no massive index keyed by the digest values. Instead,
there is a compact similarity search structure. The absence of such
a digests index, which commonly does not enable to support small
digest segment sizes for large repositories, enables the similarity
based deduplication approach to support small digest segment sizes
also for considerably large repositories.
[0038] The ability to support small digest segment sizes enables
the similarity based deduplication approach to gain a considerable
advantage over other deduplication approaches. In the similarity
based deduplication approach the digests are stored in a linear
form, which is independent of the deduplicated form by which the
data these digests describe is stored, and in the sequence of their
occurrence in the data. This method of storage enables efficient
retrieval of linear sections of digests, independent of
fragmentation characterizing deduplicated storage forms, and thus
low on input/output (IO) and computational resource consumption.
However, while in this approach retrieval of digests is efficient,
the amount of storage consumed by the stored digests is
considerably large and potentially prohibitive.
[0039] To illustrate the large amount of storage consumed by
digests, consider the following example: Assuming a 1 KB average
digest segment size, a digest value of size 32 bytes (e.g. SHA-256)
and a position field of size 8 bytes, then the digests will consume
3.90625% of the data they describe. Considering a factoring ratio
of 1:8 and a compression ratio of 1:2 (both are common in backup
environments), then the digests will consume additional physical
storage space of 8.times.2.times.3.90625%=62.5% of the physical
data they describe. Thus for example, for stored data of physical
size 1024 TB, the additional physical storage consumed by digests
would be 640 TB. This is clearly prohibitive. As such, this is a
make-or-break problem for the similarity based deduplication
approach. Therefore, a solution is required to enable reducing the
storage consumed by digests in similarity based deduplication
systems. In one embodiment, a solution is described herein,
providing all the benefits of small digest segment sizes, while
keeping the storage consumed by the digests considerably low.
[0040] In one embodiment, in the similarity based deduplication
approach described herein, a stream of input data is partitioned
into chunks (e.g. of size 16 MB), and each chunk is processed in
two main steps. In the first step a similarity search process is
applied, and positions of the most similar reference data in the
repository are found. Within this step both similarity search
elements and digest segments boundaries are calculated for the
input chunk, based on a single linear calculation of rolling hash
values. Digest values are calculated for the input chunk based on
the produced segmentation, and stored in memory in the sequence of
their occurrence in the input data. The positions of similar data
are then used to lookup the digests of the similar reference data
and load these digests into memory, also in a sequential form.
Then, the input digests are matched with the reference digests to
form data matches.
[0041] When deduplication of an input chunk of data is complete,
the digests associated with the input chunk of data are stored in
the repository, to serve as reference digests for subsequent input
data. The digests are stored in a linear form, which is independent
of the deduplicated form by which the data that these digests
describe is stored, and in the sequence of their occurrence in the
data. This method of storage enables efficient retrieval of
sections of digests, independent of fragmentation characterizing
deduplicated storage forms, and thus low on IO and computational
resource consumption. However, without an appropriate solution, the
storage consumption of the digests will become prohibitive, as
elaborated previously.
[0042] Thus, as described herein, a key idea underlying the present
invention is that if the digests stored in a repository will be
correlative to the factored size of the data in the repository,
rather than to the total (also denoted as nominal) data size in the
repository, then the digests consumed space is sustainable. In the
above example, if the stored digests will be correlative to the
factored size of the data, then the digests will consume
2.times.3.90625%=7.8125% of the physical storage space, instead of
62.5%. The saving in this example is 54.6875% of the physical
storage space. Namely, for a 1024 TB physical repository size, the
total storage savings is 560 TB. The formula for the storage saving
percent of the physical repository size is: ((1/factoring
ratio)-1).times.(1/compression ratio).times.digests ratio.
[0043] At this point, a solution is required to address the issue
of how to transform the digests stored to be correlative to the
factored size of the data in a repository (rather than correlative
to the nominal data size). To solve this problem, a further key
solution is introduced. In one embodiment, when deduplication of a
chunk of input data is complete, the matches formed within the
deduplication process to repository data are known. In a backup
environment, the probability that subsequent input data will form
large matches with the latest ingested data is considerably higher
than the probability that subsequent input data will form large
matches with repository data that was already matched with later
ingested data. For this reason, similarity elements of repository
data that was already matched with later ingested data, may be
removed from the similarity search structure, so that references to
such data will not be produced in further similarity search
processes. Therefore, a key solution in the present invention is
that by removing digests of data that was already matched with
later ingested data, the digests stored in a repository become
correlative to the size of the data patterns which are unique in
the repository, or in other words, the digests stored in a
repository become correlative to the size of the factored data in
the repository (instead of the size of the nominal data).
[0044] In one embodiment, the present invention provides additional
beneficial properties. With the present invention, the storage
saving results are predictable, since these results depend on a
deterministic digests to data ratio and on the factoring and
compression ratios, which can be predicted. This facilitates
simpler storage management. Another beneficial property is that the
run-time costs of the present invention are very low. This is
because tracking of repository data that was already matched, and
removing sequences of digests, are efficient operations in
similarity based deduplication.
[0045] Thus, in one embodiment, the present invention reduces the
storage space consumed by digests to be correlative to the factored
repository size, which is a fraction of the original storage
consumption had it been correlative to the nominal repository size.
This solution solves a critical storage problem in similarity based
deduplication. In one embodiment, the present invention enables to
gain the deduplication benefits of small digest segment sizes,
while keeping the scalability and efficiency of the deduplication
system.
[0046] In one embodiment, the present invention removes digests of
redundant repository data, to make the digests storage consumption
correlative to the factored size of the data in the repository,
rather than to the total (nominal) data size in the repository. In
one embodiment, by way of example only, a deduplication process
includes calculating digest values for input data. The digests
values are used to locate matches with data stored in a repository.
The digest values are stored in the repository. The digest values
of the data stored in the repository that is determined to be
redundant with the input data are removed. The input digest values
are stored in a repository linearly in the sequence of their
occurrence in the data. The input digest values are stored in a
repository in a form which is independent of the form by which the
data that these digest values describe is stored. The repository
data that is used to produce matches with input data is determined
to be redundant with the input data.
[0047] In one embodiment, the present invention partitions the
nominal data into chunks (e.g. of size 16 MB), and the chunks are
grouped into sets of predefined number of chunks, denoted as chunk
sets. In one embodiment, the present invention keeps track of the
number of similarity elements currently in the similarity search
structure, associated with each chunk set in the repository, and
when this number of a specific chunk set becomes lower than a
threshold, the digests associated with that chunk set are removed
from the repository. In one embodiment, by way of example only, a
deduplication process includes calculating digests for the input
data and storing said digests in sets corresponding to the chunk
sets. Similarity elements are calculated for the input data and the
similarity elements are stored in a similarity search structure.
For each chunk set, the number of similarity elements associated
with the chunk set, which are currently contained in the similarity
search structure, is maintained. When this number of a specific
chunk set becomes lower than a threshold, the digests set
associated with that chunk set is removed from the repository.
[0048] In one embodiment, the chunk sets are non-overlapping and
cover all the chunks. The similarity elements are used to find
repository data, which is similar to the input data, and input and
repository digests are used to calculate data matches. The
similarity elements of repository data that was matched with later
ingested data are removed from the similarity search structure. For
each chunk set enclosing matched repository data, the number of
similarity elements, which were removed from the similarity search
structure for that chunk set, is subtracted from the maintained
number of similarity elements associated with the chunk set in the
similarity search structure.
[0049] Turning now to FIG. 1, exemplary architecture 10 of a
computing system environment is depicted. The computer system 10
includes central processing unit (CPU) 12, which is connected to
communication port 18 and memory device 16. The communication port
18 is in communication with a communication network 20. The
communication network 20 and storage network may be configured to
be in communication with server (hosts) 24 and storage systems,
which may include storage devices 14. The storage systems may
include hard disk drive (HDD) devices, solid-state devices (SSD)
etc., which may be configured in a redundant array of independent
disks (RAID). The operations as described below may be executed on
storage device(s) 14, located in system 10 or elsewhere and may
have multiple memory devices 16 working independently and/or in
conjunction with other CPU devices 12. Memory device 16 may include
such memory as electrically erasable programmable read only memory
(EEPROM) or a host of related devices. Memory device 16 and storage
devices 14 are connected to CPU 12 via a signal-bearing medium. In
addition, CPU 12 is connected through communication port 18 to a
communication network 20, having an attached plurality of
additional computer host systems 24. In addition, memory device 16
and the CPU 12 may be embedded and included in each component of
the computing system 10. Each storage system may also include
separate and/or distinct memory devices 16 and CPU 12 that work in
conjunction or as a separate memory device 16 and/or CPU 12.
[0050] FIG. 2 is an exemplary block diagram 200 showing a hardware
structure of a data storage system in a computer system according
to the present invention. Host computers 210, 220, 225, are shown,
each acting as a central processing unit for performing data
processing as part of a data storage system 200. The cluster
hosts/nodes (physical or virtual devices), 210, 220, and 225 may be
one or more new physical devices or logical devices to accomplish
the purposes of the present invention in the data storage system
200. In one embodiment, by way of example only, a data storage
system 200 may be implemented as IBM.RTM. ProtecTIER.RTM.
deduplication system TS7650G.TM.. A Network connection 260 may be a
fibre channel fabric, a fibre channel point to point link, a fibre
channel over ethernet fabric or point to point link, a FICON or
ESCON I/O interface, any other I/O interface type, a wireless
network, a wired network, a LAN, a WAN, heterogeneous, homogeneous,
public (i.e. the Internet), private, or any combination thereof.
The hosts, 210, 220, and 225 may be local or distributed among one
or more locations and may be equipped with any type of fabric (or
fabric channel) (not shown in FIG. 2) or network adapter 260 to the
storage controller 240, such as Fibre channel, FICON, ESCON,
Ethernet, fiber optic, wireless, or coaxial adapters. Data storage
system 200 is accordingly equipped with a suitable fabric (not
shown in FIG. 2) or network adaptor 260 to communicate. Data
storage system 200 is depicted in FIG. 2 comprising storage
controllers 240 and cluster hosts 210, 220, and 225. The cluster
hosts 210, 220, and 225 may include cluster nodes.
[0051] To facilitate a clearer understanding of the methods
described herein, storage controller 240 is shown in FIG. 2 as a
single processing unit, including a microprocessor 242, system
memory 243 and nonvolatile storage ("NVS") 216. It is noted that in
some embodiments, storage controller 240 is comprised of multiple
processing units, each with their own processor complex and system
memory, and interconnected by a dedicated network within data
storage system 200. Storage 230 (labeled as 230a, 230b, and 230n in
FIG. 3) may be comprised of one or more storage devices, such as
storage arrays, which are connected to storage controller 240 (by a
storage network) with one or more cluster hosts 210, 220, and 225
connected to each storage controller 240.
[0052] In some embodiments, the devices included in storage 230 may
be connected in a loop architecture. Storage controller 240 manages
storage 230 and facilitates the processing of write and read
requests intended for storage 230. The system memory 243 of storage
controller 240 stores program instructions and data, which the
processor 242 may access for executing functions and method steps
of the present invention for executing and managing storage 230 as
described herein. In one embodiment, system memory 243 includes, is
in association with, or is in communication with the operation
software 250 for performing methods and operations described
herein. As shown in FIG. 2, system memory 243 may also include or
be in communication with a cache 245 for storage 230, also referred
to herein as a "cache memory", for buffering "write data" and "read
data", which respectively refer to write/read requests and their
associated data. In one embodiment, cache 245 is allocated in a
device external to system memory 243, yet remains accessible by
microprocessor 242 and may serve to provide additional security
against data loss, in addition to carrying out the operations as
described in herein.
[0053] In some embodiments, cache 245 is implemented with a
volatile memory and non-volatile memory and coupled to
microprocessor 242 via a local bus (not shown in FIG. 2) for
enhanced performance of data storage system 200. The NVS 216
included in data storage controller is accessible by microprocessor
242 and serves to provide additional support for operations and
execution of the present invention as described in other figures.
The NVS 216, may also referred to as a "persistent" cache, or
"cache memory" and is implemented with nonvolatile memory that may
or may not utilize external power to retain data stored therein.
The NVS may be stored in and with the cache 245 for any purposes
suited to accomplish the objectives of the present invention. In
some embodiments, a backup power source (not shown in FIG. 2), such
as a battery, supplies NVS 216 with sufficient power to retain the
data stored therein in case of power loss to data storage system
200. In certain embodiments, the capacity of NVS 216 is less than
or equal to the total capacity of cache 245.
[0054] Storage 230 may be physically comprised of one or more
storage devices, such as storage arrays. A storage array is a
logical grouping of individual storage devices, such as a hard
disk. In certain embodiments, storage 230 is comprised of a JBOD
(Just a Bunch of Disks) array or a RAID (Redundant Array of
Independent Disks) array. A collection of physical storage arrays
may be further combined to form a rank, which dissociates the
physical storage from the logical configuration. The storage space
in a rank may be allocated into logical volumes, which define the
storage location specified in a write/read request.
[0055] In one embodiment, by way of example only, the storage
system as shown in FIG. 2 may include a logical volume, or simply
"volume," may have different kinds of allocations. Storage 230a,
230b and 230n are shown as ranks in data storage system 200, and
are referred to herein as rank 230a, 230b and 230n. Ranks may be
local to data storage system 200, or may be located at a physically
remote location. In other words, a local storage controller may
connect with a remote storage controller and manage storage at the
remote location. Rank 230a is shown configured with two entire
volumes, 234 and 236, as well as one partial volume 232a. Rank 230b
is shown with another partial volume 232b. Thus volume 232 is
allocated across ranks 230a and 230b. Rank 230n is shown as being
fully allocated to volume 238--that is, rank 230n refers to the
entire physical storage for volume 238. From the above examples, it
will be appreciated that a rank may be configured to include one or
more partial and/or entire volumes. Volumes and ranks may further
be divided into so-called "tracks," which represent a fixed block
of storage. A track is therefore associated with a given volume and
may be given a given rank.
[0056] The storage controller 240 may include a data duplication
module 255, a similarity index module 257 (e.g., a similarity
search structure), and a similarity search module 259. The data
duplication module 255, the similarity index module 257, and the
similarity search module 259 may work in conjunction with each and
every component of the storage controller 240, the hosts 210, 220,
225, and storage devices 230. The data duplication module 255, the
similarity index module 257, and the similarity search module 259
may be structurally one complete module or may be associated and/or
included with other individual modules. The data duplication module
255, the similarity index module 257, and the similarity search
module 259 may also be located in the cache 245 or other
components.
[0057] The storage controller 240 includes a control switch 241 for
controlling the fiber channel protocol to the host computers 210,
220, 225, a microprocessor 242 for controlling all the storage
controller 240, a nonvolatile control memory 243 for storing a
microprogram (operation software) 250 for controlling the operation
of storage controller 240, data for control, cache 245 for
temporarily storing (buffering) data, and buffers 244 for assisting
the cache 245 to read and write data, a control switch 241 for
controlling a protocol to control data transfer to or from the
storage devices 230, the data duplication module 255, the
similarity index module 257, and the similarity search module 259,
in which information may be set. Multiple buffers 244 may be
implemented with the present invention to assist with the
operations as described herein. In one embodiment, the cluster
hosts/nodes, 210, 220, 225 and the storage controller 240 are
connected through a network adaptor (this could be a fibre channel)
260 as an interface i.e., via at least one switch called
"fabric."
[0058] In one embodiment, the host computers or one or more
physical or virtual devices, 210, 220, 225 and the storage
controller 240 are connected through a network (this could be a
fibre channel) 260 as an interface i.e., via at least one switch
called "fabric." In one embodiment, the operation of the system
shown in FIG. 2 will be described. The microprocessor 242 may
control the memory 243 to store command information from the host
device (physical or virtual) 210 and information for identifying
the host device (physical or virtual) 210. The control switch 241,
the buffers 244, the cache 245, the operating software 250, the
microprocessor 242, memory 243, NVS 216, data duplication module
255, the similarity index module 257, and the similarity search
module 259 are in communication with each other and may be separate
or one individual component(s). Also, several, if not all of the
components, such as the operation software 250 may be included with
the memory 243. Each of the components within the devices shown may
be linked together and may be in communication with each other for
purposes suited to the present invention. As mentioned above, the
data duplication module 255, the similarity index module 257, and
the similarity search module 259 may also be located in the cache
245 or other components. As such, the data duplication module 255,
the similarity index module 257, and the similarity search module
259 maybe used as needed, based upon the storage architecture and
users preferences.
[0059] As mentioned above, in one embodiment, the input data is
partitioned into large fixed size chunks (e.g. 16 MB), and a
similarity search procedure is applied for each input chunk. A
similarity search procedure calculates compact similarity elements,
which may also be referred to as similarity elements, based on the
input chunk of data, and searches for matching similarity elements
stored in a compact search structure (i.e. index) in the
repository. The size of the similarity elements stored per each
chunk of data is typically 32 bytes (where the chunk size is a few
megabytes), thus making the search structure storing the similarity
elements very compact and simple to maintain and search within.
[0060] The similarity elements are calculated by calculating
rolling hash values on the chunk's data, namely producing a rolling
hash value for each consecutive window of bytes in a byte offset,
and then selecting specific hash values and associated positions
(not necessarily the exact positions of these hash values) to be
the similarity elements of the chunk.
[0061] One important aspect and novelty provided by the present
invention is that a single linear calculation of rolling hash
values, which is a computationally expensive operation, serves as
basis for calculating both the similarity elements of a chunk (for
a similarity search) and the segmentation of the chunk's data into
digest blocks (for finding exact matches). Each rolling hash value
is added to the calculation of the similarity elements as well as
to the calculation of the digest blocks segmentation. After being
added to the two calculations, a rolling hash value can be
discarded, as the need to store the rolling hash values is
minimized or eliminated. This algorithmic element provides
significant efficiency and reduction of CPU consumption, as well as
considerable performance improvement.
[0062] In one embodiment, the similarity search procedure of the
present invention produces two types of output. The first type of
output is a set of positions of the most similar reference data in
the repository. The second type of output is the digests of the
input chunk, comprising of the segmentation to digest blocks and
the digest values corresponding to the digest blocks, where the
digest values are calculated based on the data of the digest
blocks.
[0063] In one embodiment, the digests are stored in the repository
in a form that corresponds to the digests occurrence in the data.
Given a position in the repository and size of a section of data,
the location in the repository of the digests corresponding to that
interval of data is efficiently determined. The positions produced
by the similarity search procedure are then used to lookup the
stored digests of the similar reference data, and to load these
reference digests into memory. Then, rather than comparing data,
the input digests and the loaded reference digests are matched. The
matching process is performed by loading the reference digests into
a compact search structure of digests in memory, and then for each
input digest, querying the search structure of digests for
existence of that digest value. Search in the search structure of
digests is performed based on the digest values. If a match is
found, then the input data associated with that digest is
determined to be found in the repository and the position of the
input data in the repository is determined based on the reference
digest's position in the repository. In this case, the identity
between the input data covered by the input digest, and the
repository data covered by the matching reference digest, is
recorded. If a match is not found then the input data associated
with that digest is determined to be not found in the repository,
and is recorded as new data. In one embodiment, the similarity
search structure is a global search structure of similarity
elements, and a memory search structure of digests is a local
search structure of digests in memory.
[0064] FIG. 3 is a flowchart illustrating an exemplary method 300
for digest retrieval based on similarity search in deduplication
processing in a data deduplication system in which aspects of the
present invention may be realized. The method 300 begins (step
302). The method 300 partitions input data into data chunks (step
304). The input data may be partitioned into fixed sized data
chunks. The method 300 calculates, for each of the data chunks,
similarity elements, digest block boundaries, and corresponding
digest values are calculated (step 306). The method 300 searches
for matching similarity elements in a search structure (i.e. index)
for each of the data chunks (which may be fixed size data chunks)
(step 308). The positions of the similar data in a repository
(e.g., a repository of data) are located (step 310). The method 300
uses the positions of the similar data to locate and load into
memory stored digest values and corresponding stored digest block
boundaries of the similar data in the repository (step 312). The
method 300 matches the digest values and the corresponding digest
block boundaries of the input data with the stored digest values
and the corresponding stored digest block boundaries to find data
matches (step 314). The method 300 ends (step 316).
[0065] FIG. 4 is a flowchart illustrating an exemplary alternative
method 400 for digest retrieval based on similarity search in
deduplication processing in a data deduplication system in which
aspects of the present invention may be realized. The method 400
begins (step 402). The method 400 partitions the input data into
chunks (e.g., partitions the input data into large fixed size
chunks) (step 404), and for an input data chunk calculates rolling
hash values, similarity elements, digest block boundaries, and
digest values based on data of the input data chunk (step 406). The
method 400 searches for similarity elements of the input data chunk
in a similarity search structure (i.e. index) (step 408 and 410).
The method 400 determines if there are enough or a sufficient
amount of matching similarity elements (step 412). If not enough
matching similarity elements are found then the method 400
determines that no similar data is found in the repository for the
input data chunk, and the data of the input chunk is stored in a
repository (step 414) and then the method 400 ends (step 438). If
enough similarity elements are found, then for each similar data
interval found in a repository, the method 400 determines the
position and size of each similar data interval in the repository
(step 416). The method 400 locates the digests representing the
similar data interval in the repository (step 418). The method 400
loads these digests into a search data structure of digests in
memory (step 420). The method 400 determines if there are any
additional similar data intervals (step 422). If yes, the method
400 returns to step 416. If no, the method 400 considers each
digest of the input data chunk (step 424). The method 400
determines if the digest value exists in the memory search
structure of digests (step 426). If yes, the method 400 records the
identity between the input data covered by the digest and the
repository data having the matching digest value (step 428). If no,
the method 400 records that the input data covered by the digest is
not found in the repository (step 430). From both steps 428 and
430, the method 400 determines if there are additional digests of
the input data chunk (step 432). If yes, the method 400 returns to
step 424. If no, method 400 removes the similarity elements of the
matched data in the repository from the similarity search structure
(step 434 and step 410). The method 400 adds the similarity
elements of the input data chunk to the similarity search structure
(step 436). The method 400 ends (step 438).
[0066] FIG. 5 is a flowchart illustrating an exemplary method 500
for efficient calculation of both similarity search values and
boundaries of digest blocks using a single linear calculation of
rolling hash values in a data deduplication system in which aspects
of the present invention may be realized. The method 500 begins
(step 502). The method 500 partitions input data into data chunks
(steps 504). The data chunks may be fixed sized data chunks. The
method 500 considers each consecutive window of bytes in a byte
offset in the input data (step 506). The method 500 determines if
there is an additional consecutive window of bytes to be processed
(step 508). If yes, the method 500 calculates a rolling hash value
based on the data of the consecutive window of bytes (step 510).
The method 500 contributes the rolling hash value to the
calculation of the similarity values and to the calculation of the
digest blocks segmentations (i.e., the digest block boundaries)
(step 512). The method 500 discards the rolling hash value (step
514), and returns to step 506. If no, the method 500 concludes the
calculation of the similarity elements and of the digest blocks
segmentation, producing the final similarity elements and digest
blocks segmentation of the input data (step 516). The method 500
calculates digest values based on the digest blocks segmentation,
wherein each digest block is assigned with a corresponding digest
value (step 518). The similarity elements are used to search for
similar data in the repository (step 520). The digest blocks and
corresponding digest values are used for matching with digest
blocks and corresponding digest values stored in a repository for
determining data in the repository which is identical to the input
data (step 522). The method 500 ends (step 524).
[0067] Turning now to FIGS. 6-9, nominal data is partitioned into
chunks (e.g. of size 16 MB), and the chunks are grouped into sets
of predefined number of chunks, denoted as chunk sets. The chunk
sets are non-overlapping, and cover all the chunks. The number of
chunks in a set is at least 1, but is normally defined to be
larger, e.g. 16 chunks (thus in this example a chunk set represents
256 MB of data). The significance of the chunk sets is twofold.
First, for each chunk set, relevant information is maintained to
enable determination of digests for removal. Second, digests are
stored in the repository in physical sets that correspond to the
chunk sets. Namely, for each chunk set there is an associated
digests set stored in the repository. These digests sets can be
efficiently located, retrieved, and removed when required, which
architecture is illustrated in FIG. 6 below.
[0068] FIG. 6 is a block diagram illustrating a compact data
structure 600 containing a record for each chunk set in which
aspects of the present invention may be realized. A compact data
structure 600, denoted as the chunk sets data structure 606,
contains a record 608 for each chunk set 602, where each record 608
(records for chunk set A, B, C, and D) includes an identification
of the chunk set 610, and the number of similarity elements 612
associated with the chunk set 602, which are currently contained in
the similarity search structure. For example, the records of the
chunk sets 602 illustrate that the chunk set identification (ID)
for chunk set A has the number Na of similarity elements, chunk set
B has the number Nb of similarity elements, chunk set C has the
number Nc of similarity elements, and chunk set D has the number Nd
of similarity elements. As mentioned above, digests 604
(illustrated as 604A and 604B) are stored in the repository in
physical sets that correspond to the chunk sets 602. Namely, for
each chunk set 602 there is an associated digests set 604 stored in
the repository. These digests sets 604 can be efficiently located,
retrieved, and removed when required. Considering 16 bytes per
record (without any compaction), then in the above example this
data structure contains 1 byte for each 16 MB of data, which is
very compact. For example, for a repository of 1 PB physical
storage and a deduplication ratio of 1:16 (i.e. 16 PB of nominal
data), the size of this data structure is 1 GB.
[0069] In one embodiment, during deduplication processing of the
chunks in an input chunk set, a record is kept in memory of the
number of similarity elements that are inserted into the similarity
search structure for the chunks in the chunk set. When
deduplication processing of the chunk set is complete, the total
number of similarity elements that were inserted into the
similarity search structure for the chunk set is therefore known.
At this point, a record is added to the chunk sets data structure,
containing an identification of the chunk set and the number of
similarity elements that were inserted into the similarity search
structure for the chunk set.
[0070] Further, during deduplication processing of a chunk set, an
additional piece of information is maintained in memory. This
information is a list of repository chunk sets that enclose
repository intervals based on which the deduplication process of
the chunks in the current chunk set produced data matches. For each
repository interval that was matched with input chunks, the
identification of its enclosing chunk set is known. For each data
match that was produced, the deduplication process calculates the
similarity elements that occur in the matched section of repository
data, and then removes these similarity elements from the
similarity search structure. The total number of similarity
elements that were removed from the similarity search structure,
for each repository chunk set enclosing matched repository data,
are then updated in the list maintained for the current chunk set.
Namely, each entry in the list contains an identification of a
chunk set enclosing repository data that was matched with the
current chunk set, as well as the number of similarity elements of
the repository chunk set that were removed from the similarity
search structure (by the deduplication process of the current chunk
set).
[0071] When deduplication processing of a current chunk set is
complete, the list of repository chunk sets is scanned, and for
each entry, the number of similarity elements which were removed
from the similarity search structure for the specific repository
chunk set (specified in the entry), is then subtracted from the
number of similarity elements recorded for that specific chunk set
in the chunk sets data structure (illustrated in FIG. 6). The
result of this subtraction is the current number of similarity
elements of that specific chunk set in the similarity search
structure. This number is updated in the chunk set's record within
the chunk sets data structure. By applying this process, the
records of the relevant chunk sets in the chunk set data structure,
now reflect the updated number of similarity elements of each chunk
set in the similarity search structure. Within this process, the
resulting number of similarity elements for each repository chunk
set being processed, is evaluated. If this number is lower than a
predefined threshold, then the representation of that repository
chunk set in the similarity search structure is determined to be
sufficiently low, and the process therefore proceeds to remove the
digests associated with that chunk set from the repository. This
enables the size of the digests stored in a repository to be
maintained correlative to the factored data size. The threshold
value, with which the resulting numbers of similarity elements for
each repository chunk set are compared, can be specific for each
repository chunk set, rather than fixed for all chunk sets. This
threshold can be specified by means of a percent out of the initial
number of similarity elements inserted per chunk set.
[0072] In one embodiment, as illustrated in FIG. 7, the present
invention removes digests of redundant repository data, to make the
digests storage consumption correlative to the factored size of the
data in the repository, rather than to the total (nominal) data
size in the repository. In one embodiment, by way of example only,
a deduplication process includes calculating digest values for
input data. The digest values of the input data and stored digest
values of data stored in a repository are used to locate matches
with the stored data. The digest values of the input data are
stored in the repository. The digest values of the data stored in
the repository that is determined to be redundant with the input
data are removed. The digest values of the input data are stored in
a repository linearly in the sequence of their occurrence in the
data. The digest values of the input data are stored in a
repository in a form which is independent of the form by which the
data that these digest values describe is stored. The repository
data that is used to produce matches with input data is determined
to be redundant with the input data.
[0073] FIG. 7 is a flowchart illustrating an exemplary method 700
for reducing digests storage consumption in a data deduplication
system in which aspects of the present invention may be realized.
In other words, the method 700 provides for removing digests of
redundant repository data, to make the digests storage consumption
correlative to the factored size of the data in the repository,
rather than to the total (nominal) data size in the repository in a
data deduplication system. The method 700 begins (step 702). The
method 700 calculates digest values for input data (step 704). The
method 700 uses the digest values of the input data and stored
digest values of data stored in a repository to locate matches with
the stored data (step 706). The method 700 stores the digest values
of the input data in the repository (step 708). The method 700
removes the digest values of the data stored in the repository that
is determined to be redundant with the input data (step 710). The
method 700 ends (step 712).
[0074] FIG. 8 is a flowchart illustrating an alternative exemplary
method 800 for reducing digests storage consumption in a data
deduplication system in which aspects of the present invention may
be realized. The method 800 beings (step 802) by partitioning input
data into data chunks and grouping the data chunks into data chunk
sets (step 804). The method 800 performs deduplication processing
of the input data chunk set, and updates the information on
insertion and deletion of similarity elements to and from a
similarity search structure (e.g., a similarity index) (step 806).
During the processing within step 806, the method 800 maintains a
record of the number of similarity elements inserted into the
similarity search structure for the input chunk set (step 810), and
a list is maintained of repository chunk sets with which data
matches were formed, wherein each entry in the list includes a
number of similarity elements of the associated chunk set that were
removed from the similarity search structure (step 812). Next, the
method 800 completes the deduplication of the input chunk set, and
updates in the chunk sets data structure (step 814) the number of
similarity elements (step 810) that were inserted into the
similarity search structure for the current input chunk set (step
808). The method 800 then determines if there is an additional
entry in the list of step 812 (step 816). If no, the method 800
ends (step 826). If yes, the method 800 subtracts the number of
similarity elements that were removed from the similarity search
structure for the specific repository chunk set, from the number of
similarity elements recorded for that specific chunk set in the
chunk sets data structure, and updates the result in the chunk
set's entry within the chunk sets data structure of step 814 (step
818). The method 800 then determines if the resulting number of
similarity elements is lower than a predefined threshold (step
820). If no, the method 800 returns to step 816. If yes, the method
800 removes the digests associated with that chuck set from the
repository where digests are stored in step 824 (step 822). The
method 800 then returns to step 816. If the answer on step 816 is
no, then the method 800 ends (step 826).
[0075] FIG. 9 is a flowchart illustrating an exemplary method 900
for track of the number of similarity elements of a chunk set
currently in a similarity search structure, for removing the
digests associated with that chunk set from the repository in a
data deduplication system in which aspects of the present invention
may be realized. In other words, the method 900 provides for track
of the number of similarity elements currently in a similarity
search structure, associated with each repository chunk set, and
when this number of a specific chunk set becomes lower than a
threshold (e.g., a predetermined threshold), removing the digests
associated with that chunk set from the repository in a data
deduplication system. The method 900 begins (step 902). The method
900 partitions input data into data chunks and groups the data
chunks into data chunk sets (step 904). The method 900 calculates
digests for the input data and stores the digests in sets
corresponding to the chunk sets (step 906). The method 900
calculates similarity elements for the input data and stores the
similarity elements in the similarity search structure (step 908).
The method 900 maintains, for each chunk set, a number of
similarity elements associated with the chunk set, which are
currently contained in the similarity search structure (step 910).
Then, when this number of a specific chunk set becomes lower than a
threshold the digest set associated with that chunk set is removed
from the repository (step 912). The method 900 then ends (step
914).
[0076] As will be appreciated by one skilled in the art, aspects of
the present invention may be embodied as a system, method or
computer program product. Accordingly, aspects of the present
invention may take the form of an entirely hardware embodiment, an
entirely software embodiment (including firmware, resident
software, micro-code, etc.) or an embodiment combining software and
hardware aspects that may all generally be referred to herein as a
"circuit," "module" or "system." Furthermore, aspects of the
present invention may take the form of a computer program product
embodied in one or more computer readable medium(s) having computer
readable program code embodied thereon.
[0077] Any combination of one or more computer readable medium(s)
may be utilized. The computer readable medium may be a computer
readable signal medium or a computer readable storage medium. A
computer readable storage medium may be, for example, but not
limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer readable
storage medium may be any tangible medium that may contain, or
store a program for use by or in connection with an instruction
execution system, apparatus, or device.
[0078] Program code embodied on a computer readable medium may be
transmitted using any appropriate medium, including but not limited
to wireless, wired, optical fiber cable, RF, etc., or any suitable
combination of the foregoing. Computer program code for carrying
out operations for aspects of the present invention may be written
in any combination of one or more programming languages, including
an object oriented programming language such as Java, Smalltalk,
C++ or the like and conventional procedural programming languages,
such as the "C" programming language or similar programming
languages. The program code may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider).
[0079] Aspects of the present invention have been described above
with reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems) and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, may be implemented by computer program
instructions. These computer program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or
blocks.
[0080] These computer program instructions may also be stored in a
computer readable medium that may direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks. The computer
program instructions may also be loaded onto a computer, other
programmable data processing apparatus, or other devices to cause a
series of operational steps to be performed on the computer, other
programmable apparatus or other devices to produce a computer
implemented process such that the instructions which execute on the
computer or other programmable apparatus provide processes for
implementing the functions/acts specified in the flowchart and/or
block diagram block or blocks.
[0081] The flowchart and block diagrams in the above figures
illustrate the architecture, functionality, and operation of
possible implementations of systems, methods and computer program
products according to various embodiments of the present invention.
In this regard, each block in the flowchart or block diagrams may
represent a module, segment, or portion of code, which comprises
one or more executable instructions for implementing the specified
logical function(s). It should also be noted that, in some
alternative implementations, the functions noted in the block may
occur out of the order noted in the figures. For example, two
blocks shown in succession may, in fact, be executed substantially
concurrently, or the blocks may sometimes be executed in the
reverse order, depending upon the functionality involved. It will
also be noted that each block of the block diagrams and/or
flowchart illustration, and combinations of blocks in the block
diagrams and/or flowchart illustration, may be implemented by
special purpose hardware-based systems that perform the specified
functions or acts, or combinations of special purpose hardware and
computer instructions.
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